3D occupancy perception holds a pivotal role in recent vision-centric
autonomous driving systems by converting surround-view images into integrated
geometric and semantic representations within dense 3D grids. Nevertheless,
current models still encounter two main challenges: modeling depth accurately
in the 2D-3D view transformation stage, and overcoming the lack of
generalizability issues due to sparse LiDAR supervision. To address these
issues, this paper presents GEOcc, a Geometric-Enhanced Occupancy network
tailored for vision-only surround-view perception. Our approach is three-fold:
1) Integration of explicit lift-based depth prediction and implicit
projection-based transformers for depth modeling, enhancing the density and
robustness of view transformation. 2) Utilization of mask-based encoder-decoder
architecture for fine-grained semantic predictions; 3) Adoption of
context-aware self-training loss functions in the pertaining stage to
complement LiDAR supervision, involving the re-rendering of 2D depth maps from
3D occupancy features and leveraging image reconstruction loss to obtain denser
depth supervision besides sparse LiDAR ground-truths. Our approach achieves
State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image
resolution needed and the most weightless image backbone compared with current
models, marking an improvement of 3.3% due to our proposed contributions.
Comprehensive experimentation also demonstrates the consistent superiority of
our method over baselines and alternative approaches.

本文提出了 GEOcc，一种适用于仅使用视觉的全景视图感知的几何增强占用网络，它通过将环视图图像转换为密集的三维网格内的集成几何和语义表示来实现对三维占用的感知，解决了目前模型面临的两个主要挑战：在二维至三维视图变换阶段准确建模深度，以及由于稀疏的 LiDAR 监督引起的泛化问题的缺乏。